Below shows:
min_bic <- 100000
for(i in 2:7){
lc <- poLCA(f, AUS, nclass=i, maxiter=3000,
tol=1e-5, na.rm=FALSE,
nrep=10, verbose=TRUE, calc.se=TRUE)
if(lc$bic < min_bic){
min_bic <- lc$bic
LCA_best_model<-lc
}
}
print(LCA_best_model)
Conditional item response (column) probabilities,
by outcome variable, for each class (row)
$tax
Pr(1) Pr(2) Pr(3) Pr(4) Pr(5) Pr(6) Pr(7) Pr(8) Pr(9) Pr(10)
class 1: 0.1756 0.0208 0.0903 0.0288 0.1086 0.0753 0.0965 0.0741 0.0437 0.2863
class 2: 0.0257 0.0288 0.0595 0.1047 0.3553 0.1350 0.1489 0.0897 0.0346 0.0178
class 3: 0.0028 0.0303 0.0672 0.0739 0.1374 0.1453 0.2182 0.1862 0.1188 0.0199
$religion
Pr(1) Pr(2) Pr(3) Pr(4) Pr(5) Pr(6) Pr(7) Pr(8) Pr(9) Pr(10)
class 1: 0.7301 0.0227 0.0434 0.0198 0.0756 0.0169 0.0080 0.0205 0.0110 0.0521
class 2: 0.1514 0.1858 0.1904 0.0893 0.2203 0.0724 0.0563 0.0152 0.0144 0.0046
class 3: 0.4164 0.1947 0.1566 0.0888 0.0643 0.0328 0.0255 0.0169 0.0042 0.0000
$free_election
Pr(1) Pr(2) Pr(3) Pr(4) Pr(5) Pr(6) Pr(7) Pr(8) Pr(9) Pr(10)
class 1: 0.0496 0.0024 0.0020 0.0000 0.0221 0.0060 0.0000 0.0059 0.0112 0.9007
class 2: 0.0208 0.0360 0.0430 0.0392 0.1787 0.0830 0.1968 0.1702 0.1254 0.1068
class 3: 0.0018 0.0000 0.0025 0.0008 0.0000 0.0063 0.0186 0.0880 0.2217 0.6604
$state_aid
Pr(1) Pr(2) Pr(3) Pr(4) Pr(5) Pr(6) Pr(7) Pr(8) Pr(9) Pr(10)
class 1: 0.1045 0.0245 0.0472 0.0400 0.1195 0.0384 0.0310 0.1055 0.0360 0.4533
class 2: 0.0497 0.0778 0.0712 0.0698 0.3181 0.1734 0.1217 0.1004 0.0168 0.0011
class 3: 0.0223 0.0355 0.0650 0.0691 0.0984 0.1346 0.2168 0.1794 0.1449 0.0340
$civil_rights
Pr(1) Pr(2) Pr(3) Pr(4) Pr(5) Pr(6) Pr(7) Pr(8) Pr(9) Pr(10)
class 1: 0.0921 0.0051 0.0100 0.0174 0.0759 0.0152 0.0260 0.0449 0.0229 0.6906
class 2: 0.0294 0.0765 0.0656 0.0827 0.3384 0.1512 0.1644 0.0917 0.0000 0.0000
class 3: 0.0000 0.0068 0.0168 0.0289 0.0399 0.0527 0.0915 0.2350 0.2678 0.2606
$women
Pr(1) Pr(2) Pr(3) Pr(4) Pr(5) Pr(6) Pr(7) Pr(8) Pr(9) Pr(10)
class 1: 0.0316 0.0009 0.0000 0.0000 0.0140 0.0021 0.0036 0.0025 0.0092 0.9361
class 2: 0.0116 0.0109 0.0111 0.0185 0.1493 0.0478 0.1579 0.2057 0.0992 0.2879
class 3: 0.0000 0.0049 0.0000 0.0000 0.0042 0.0000 0.0009 0.0528 0.1769 0.7603
Estimated class population shares
0.3989 0.2019 0.3991
Predicted class memberships (by modal posterior prob.)
0.3967 0.1961 0.4072
=========================================================
Fit for 3 latent classes:
=========================================================
number of observations: 1336
number of estimated parameters: 164
residual degrees of freedom: 1172
maximum log-likelihood: -12832.31
AIC(3): 25992.62
BIC(3): 26845
G^2(3): 8045.548 (Likelihood ratio/deviance statistic)
X^2(3): 4059074 (Chi-square goodness of fit)
plot(LCA_best_model)
Below shows:
min_bic <- 100000
for(i in 2:7){
lc <- poLCA(f, SWEDEN, nclass=i, maxiter=3000,
tol=1e-5, na.rm=FALSE,
nrep=10, verbose=TRUE, calc.se=TRUE)
if(lc$bic < min_bic){
min_bic <- lc$bic
LCA_best_model<- lc
}
}
LCA_best_model
Conditional item response (column) probabilities,
by outcome variable, for each class (row)
$tax
Pr(1) Pr(2) Pr(3) Pr(4) Pr(5) Pr(6) Pr(7) Pr(8) Pr(9) Pr(10)
class 1: 0.0134 0.0553 0.1039 0.0425 0.1645 0.1149 0.2246 0.2117 0.0599 0.0093
class 2: 0.0674 0.0549 0.0691 0.0660 0.0739 0.0572 0.1369 0.1936 0.1058 0.1751
$religion
Pr(1) Pr(2) Pr(3) Pr(4)
class 1: 0.3150 0.2601 0.1875 0.2374
class 2: 0.8031 0.0864 0.0456 0.0649
$free_election
Pr(1) Pr(2) Pr(3) Pr(4) Pr(5) Pr(6) Pr(7) Pr(8) Pr(9) Pr(10)
class 1: 0 0 0 0 0 0 0 0.3638 0.3458 0.2904
class 2: 0 0 0 0 0 0 0 0.0217 0.0166 0.9617
$state_aid
Pr(1) Pr(2) Pr(3) Pr(4) Pr(5) Pr(6) Pr(7) Pr(8) Pr(9) Pr(10)
class 1: 0.0125 0.0358 0.0725 0.0491 0.0912 0.1206 0.1678 0.2744 0.120 0.0562
class 2: 0.0704 0.0420 0.0395 0.0362 0.0678 0.0595 0.0866 0.1729 0.126 0.2991
$civil_rights
Pr(1) Pr(2) Pr(3) Pr(4) Pr(5) Pr(6) Pr(7) Pr(8) Pr(9) Pr(10)
class 1: 0 0 0 0 0 0 0.2511 0.3321 0.3198 0.0970
class 2: 0 0 0 0 0 0 0.0245 0.0460 0.0722 0.8573
$women
Pr(1) Pr(2) Pr(3) Pr(4) Pr(5) Pr(6) Pr(7) Pr(8) Pr(9) Pr(10)
class 1: 0 0 0 0 0 0 0 0 0.441 0.559
class 2: 0 0 0 0 0 0 0 0 0.061 0.939
Estimated class population shares
0.249 0.751
Predicted class memberships (by modal posterior prob.)
0.2417 0.7583
=========================================================
Fit for 2 latent classes:
=========================================================
number of observations: 964
number of estimated parameters: 97
residual degrees of freedom: 867
maximum log-likelihood: -6750.805
AIC(2): 13695.61
BIC(2): 14168.11
G^2(2): 2635.129 (Likelihood ratio/deviance statistic)
X^2(2): 14270.99 (Chi-square goodness of fit)
plot(LCA_best_model)
Profile plot for Sweden data (2-classes)
plot = ggplot(profile_long, aes(x = variable, y = value, group = class, color = class)) +
geom_point(size = 2.25)+
geom_line(size = 1.25) +
labs(x = NULL, y = "Mean value of the response", main = "Profile plot") +
theme_bw(base_size = 14)+
theme(axis.text.x = element_text(angle = 45, hjust = 1), legend.position = "top")
p = ggplotly(plot, tooltip = "all") %>%
layout(legend = list(orientation = "h", y = 1.2))
print(p)
NULL